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Re: Constraint-based inference? (was Re: Solving Sudoku with OWL)

From: Jeremy Wong 黃泓量 <jeremy@miko.hk>
Date: Thu, 12 Jan 2006 11:14:26 +0800
Message-ID: <005001c61726$4b9cc840$6802a8c0@miko.hk.local>
To: "Danny Ayers" <danny.ayers@gmail.com>, "Aditya Kalyanpur" <swap_adityak@yahoo.com>
Cc: Jeremy Wong ¶Àªl¶q <jeremy@1980.hk>, "Semantic Web" <semantic-web@w3.org>

> The Sudoku thread reminds me of a question (prompted by a comment from
> Tim Finin [1]) - has anyone tried doing any RDFS/OWL inference based
> on a constraints programming engine [2]?
> For highly combinative problems (like Sudoku) perhaps such a setup may
> give improved performance (maybe a hybrid might be feasible - e.g. the
> constraints part sorting out the AllDifferent kind of inferences, then
> passing the partial results to a complete DL reasoner to finish up..?)

As I know, those solvers yield multiple solutions for some problems. In some 
senses solvers work by narrowing the search space and generate solutions 
iteratively. It differs from logical inference. Taking sudoku puzzle as 
examples, some sudoku puzzles have multiple solution and some have unique 
solution. If we use a DL reasoner to solve any sudoku puzzle having multiple 
solution, it should not be able to solve the puzzle. The DL reasoner should 
only fill in some blank cells rather than all cells. If we use a solver to 
solve the puzzle, it will give you all solutions. It is more than what a DL 
reasoner should do.

Jeremy Wong 黃泓量 
Received on Thursday, 12 January 2006 03:15:48 UTC

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